Turn student AI capability into curriculum progression and visible evidence.
AI-Ready Graduate helps business schools define what students should be able to do with AI, decide where those capabilities develop, and collect evidence that students can actually demonstrate them. The framework provides a common Human+AI Fluency language while leaving programs room to adapt expectations to discipline, level, mission, and professional context.
A competency statement becomes useful when students have a path to develop it.
Many schools can describe the broad AI abilities they want graduates to possess. The harder work begins after the statement is approved. Programs still have to decide where students first encounter the capability, where they practice it with feedback, where expectations become more demanding, and what evidence will demonstrate that the learning occurred.
AI-Ready Graduate gives curriculum teams a structure for making those decisions. It connects student capability to course placement, developmental expectations, evidence artifacts, and improvement. The result is a curriculum story leadership can explain and faculty can actually implement.
Seven domains organize the work students need to learn.
The seven domains provide a practical Human+AI Fluency map. Their value comes from helping a program describe observable student performance and connect it to evidence, rather than asking every course to teach the same content or use the same tools.
| Domain | What the student learns to do | Example evidence |
|---|---|---|
| AI Foundations | Explain what AI can do, how it works in general terms, and where important limitations remain. | AI Concepts Brief |
| AI Workflows | Use AI purposefully inside a structured process with visible human decisions and review. | AI Workflow Map |
| Judgment & Verification | Evaluate claims, logic, sources, calculations, recommendations, bias, and usefulness before relying on output. | Verification Log |
| Data Stewardship | Protect confidential, proprietary, personal, and restricted information while respecting policy and ownership boundaries. | Data Stewardship Check |
| Responsible Use | Make transparent and accountable decisions about when AI use is appropriate and what risks require attention. | Responsible Use Note |
| AI Communication | Explain the role AI played, the human decisions that shaped the work, and the verification completed before submission. | AI Process Appendix |
| Professional Application | Apply AI meaningfully within a discipline, profession, workplace task, or decision context. | Professional Application Artifact |
Programs can make progression visible from early exposure to professional application.
Students do not need to reach the same level in every domain at the same point in the curriculum. The four developmental levels help faculty set appropriate expectations and show where a capability should deepen over time.
Aware
Recognizes core concepts, common uses, basic limitations, and important risks.
Informed
Uses AI purposefully in bounded situations and understands the responsibilities attached to that use.
Capable
Applies, verifies, documents, and adapts AI use to the task and surrounding context.
Integrated
Designs responsible Human+AI workflows and exercises professional judgment across more complex work.
A first-year course might introduce verification and responsible-use expectations. A major course can require students to apply those practices in discipline-specific work. Capstones, internships, and advanced experiences can ask students to integrate several domains at once and explain their decisions in professional language.
Capability becomes more credible when students leave a visible record of the work.
The evidence portfolio translates the framework into artifacts faculty can assess and students can explain. Each artifact captures a different part of Human+AI Fluency, so the program does not have to infer readiness from tool use, confidence, or a polished final product alone.
The artifacts are modular. A course can use one where it fits naturally. A program can sequence several across the curriculum. Over time, the collection gives the school direct evidence of how students use AI, where they exercise judgment, how they verify information, and whether they can explain their process responsibly.
Start at the level where the curriculum question is most consequential.
AI-Ready Graduate can support a focused assignment change, a program-level curriculum map, or a more visible student pathway. Navigate AI helps schools choose the smallest implementation that produces useful capability and evidence.
Make one capability observable
Use a domain descriptor, an evidence artifact, and the core rubric inside a priority assignment. This is a practical way to test the framework before broader curriculum work.
Build a coherent capability strand
Align outcomes, assignments, feedback, and evidence across a course or several strategically selected courses so students encounter a deliberate progression.
Map progression across the curriculum
Show where each priority capability is introduced, practiced, demonstrated, and documented. The map reveals both genuine coverage and places where the program currently relies on assumption.
The AI-Ready Graduate Pathway
For schools that want a visible, structured student experience, the Pathway provides a three-level, six-studio, three-credit sequence with approximately 120 documented learning hours. Students move from foundations and workflows into verification, data stewardship, responsible use, professional application, and more complex decision work.
- Six complete studio guides
- Student workbook and evidence portfolio kit
- Faculty resources, datasets, and ethics cases
- Annual curriculum versioning
Help students explain what they can do
Translate workflow design, verification, data decisions, and accountability into language students can use in portfolios, internships, interviews, and professional conversations.
Move from a broad aspiration to an evidence-bearing curriculum plan.
The engagement is built around curriculum decisions rather than adoption of a packaged framework. Existing institutional outcomes, courses, assessments, employer expectations, and faculty priorities shape the final design.
Define
Clarify the AI capabilities that matter for the program and the level of performance expected by graduation.
Map
Identify where students encounter, practice, and demonstrate those capabilities across the existing curriculum.
Evidence
Select or design artifacts and assessment criteria that make student judgment, process, and performance visible.
Implement
Prioritize realistic course changes, faculty support, ownership, and a sequence for piloting and improvement.
A curriculum architecture leadership can explain and faculty can use.
The exact package depends on scope, but the work is designed to leave the school with usable curriculum and evidence assets rather than a conceptual framework alone.
- Defined student AI capability expectations using the seven-domain framework as a common language.
- A curriculum map showing where capabilities are introduced, developed, and demonstrated.
- An AI-Ready Evidence Portfolio strategy and assessment approach.
- Priority assignment, course, or program implementation recommendations.
- Faculty-facing tools, student guidance, and workshop resources matched to the chosen scope.
- A leadership roadmap identifying owners, sequencing, evidence needs, and the next improvement cycle.
What should students be able to do?
Where will students learn and practice it?
What evidence will show capability?
How will findings change the curriculum?
Begin with one program and build only as far as the evidence requires.
A focused curriculum-mapping engagement provides a practical entry point. More extensive implementation is appropriate when a school wants Navigate AI to help build or pilot the evidence system, faculty supports, or a visible student pathway.
$6,500–$9,500
For one program defining graduate capability and mapping progression through the existing curriculum.
- Capability definition
- Curriculum map and gap analysis
- Evidence and implementation priorities
$9,500–$14,500
For schools moving from the map into selected assignments, evidence artifacts, faculty work, and a pilot-ready implementation plan.
- Selected artifact and rubric design
- Faculty implementation support
- Leadership evidence package
Scoped separately
For institution-wide mapping, substantial curriculum development, or adoption of the six-studio AI-Ready Graduate Pathway.
- Scope matched to programs and courses
- Pathway licensing and implementation options
- Expanded faculty and leadership support
Ranges are indicative and finalized after scope. Schools can use the AI-Ready Graduate architecture without adopting the optional Pathway curriculum.
What curriculum and academic leaders usually want to know.
Does every student need to reach the same level in all seven domains?
No. Programs set the target level based on mission, discipline, degree level, and professional expectations. The framework makes those choices visible so progression can be intentional rather than assumed.
Does AI-Ready Graduate require new courses?
No. Many schools can begin inside existing courses by mapping outcomes, revising selected assignments, and adding evidence artifacts where they fit. A separate pathway is available when a school wants a more visible student experience.
How does this connect to assurance of learning?
The framework can help define student capability, but the important next step is selecting direct evidence that supports the learning inference the program needs to make. Navigate AI can connect ARG work to the AI Assessment Evidence Sprint when a program needs deeper measure design or calibration.
Is the six-studio Pathway required?
No. It is one implementation model. Schools can use the broader ARG framework at the assignment, course, or program level and build their own curriculum around the common capability and evidence architecture.
Give the curriculum a clear answer to what students should demonstrate with AI.
Start with one program, map the capabilities that matter, and build evidence at the points where students should be able to show them.
Want a clearer student AI fluency model for your campus, GenEd, or program?
Start with your institutional context. Whether you need an embedded model, a microcourse, a bootcamp/seminar, or a pathway approach, this form is the fastest way to scope the right next step.
What leaders usually ask for first
Start with the framework and Atlas.
These resources help leaders and faculty understand the model before starting an implementation conversation.
Direct contact
Email works fine if you already know what you need or want to send context before a call.